1. Field of the Invention
This invention relates to manufacturing automation and in particular to a system and method for timely replacement of cutting tools through early prediction of cutting tool life and accurate diagnosis of tool wear using an Influence Diagram Expert System and multiple sensors.
2. Description of the Related Art
The background of this invention is described in conjunction with manufacturing automation as an example.
Automation in manufacturing is a rapidly growing field with significant impact on improved product quality, high productivity and reduced manufacturing cost. While it has been adopted successfully at different levels of manufacturing process, one of the main hindrances of completely automating any metal cutting operation is the timely replacement of cutting tools. Currently, human intervention is required to change the cutting tools at appropriate heuristically chosen intervals and typically, one machinist is assigned to supervise and replace cutting tools for a batch of automated machining centers. With adequate sensors for in-process monitoring of the tool condition, an improvement can be obtained in machining economics, especially in high volume production lines. Cutting tool condition monitoring, wear diagnostics and appropriate machine control problems have been investigated through different methodologies: real time expert system based approaches and mathematical model based approaches. While these on-line monitoring techniques provide a solution to correctly identify a damaged or worn tool, they are postmortem techniques; waiting for an appropriate decision after the tool is worn or damaged.
A tool changing operation may involve multiple tasks like procurement of a replacement tool from a centralized storage area, loading tool-setting software from the plant manager to the machine controller, dynamic re-scheduling of the production line to account for the anticipated machine down time, and so on. The present tool monitoring techniques do not provide adequate lead-time to initiate these multiple tasks in preparation for a tool-change, and hence, no significant improvement is made in reducing the downtime due to cutting tool changes. Another added limitation of present continuous on-line monitoring is the requirement of dedicated processors on each machine, primarily because of stringent response time requirements of the present day real time diagnostic systems.
In order to automate the cutting tool replacement task with adequate forewarning, a reasonably accurate estimate of the life of the cutting tool is required. As mentioned earlier, a significant advantage would be that the predicted tool life information could be utilized for dynamically re-scheduling machining operations and cutting tool replacements with a reduction in inventory and labor costs.
Mathematical model based techniques, called Taylor's tool life equation, and extensions thereto, may be applied to estimate the cutting tool life. However, these equations provide a poor estimate of the life of the cutting tool. For example, in the case of drilling, work piece material hardness is an important factor affecting the life of the drill but is not taken into account. While modified forms of Taylor's equation with corrections for work piece hardness have been applied to determine the drill tool life, such an approach is impractical in a production line since hardness for each work piece is not readily available or easily measurable. Furthermore, in some other machining situations, Taylor's model has been found unsuitable.
Therefore, it would be desirable to have a system in which a cutting tool's life can be accurately predicted during the initial use of the tool so as to provide adequate lead-time for the replacement of the tool.
In the area of cutting tool wear diagnosis, some present day techniques are pattern recognition (PR), neural nets (NN) and real-time expert systems (RTES). PR and NN, use pattern samples that are already classified based on some wear criteria and train the system to recognize this fact. This approach is called `supervised learning`. In `unsupervised learning`, no wear criteria is used and hence, the knowledge about the class to which the samples belong is not available a priori. Such an unsupervised training approach using fuzzy clustering technique has been applied to the drill tool classification problem. Using thrust and torque data from a torque dynamometer, a fuzzy classification of the state of the drill tool has been performed successfully. However, the drill is classified as worn only after the cutting edges are severely damaged. This prediction is delayed until the drill is about to fail.
The advantages of the RTES techniques are the ease of modification, the ability to select optimal control decisions by optimizing over a cost function after classifying the current state of the tool, and the facility to repetitively utilize the same inference engine while applying to different machining operations. If the diagnostic system needs to be updated to handle new combinations of machining parameters or new sensors, then in case of PR or NN techniques, the system has to be retrained with new as well as old data, a rather time consuming process. However, in case of RTES, the knowledge base can be easily updated with the new information.
Heretofore, one of the problems with the present RTES technique has been the lack of learning capability. The development and tuning of the RTES requires detailed analysis of pertinent data to extract features and subjective estimate of the conditional probability distributions that form the critical elements of the knowledge base.